Despite the increasing importance of multiple priors in various domains of economics, choice-based incentive-compatible multiple-prior elicitation remains an open problem. This paper develops a solution, comprising a preference-based identification of a subject’s probability interval for an event, and a method for eliciting it. The method applies under weak decision-theoretic assumptions, with no need for probabilistic sophistication. To demonstrate its feasibility, we implement it in three incentivized experiments on artificial and natural sources of uncertainty. Intervals elicited by our method are sensitive to the direction and amount of information, and are typically consistent with ‘objective’ probabilities where available. We find a predominance of non-degenerate probability intervals, with intervals being wider when there is less information or predictability. The probability intervals elicited with our method are similar to those stated by subjects on aggregate, suggesting that the method can provide behavioral foundations for the use of stated probability-interval techniques in the field.